Detection of Maximal Balance Clique Using Three-way Concept Lattice


Yixuan Yang, Doo-Soon Park, Fei Hao, Sony Peng, Hyejung Lee, and Min-Pyo Hong, Journal of Information Processing Systems Vol. 19, No. 2, pp. 189-202, Apr. 2023  

10.3745/JIPS.01.0094
Keywords: formal concept analysis, Maximal Balanced Clique, Signed Networks, Three-Way Concept
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Abstract

In the era marked by information inundation, social network analysis is the most important part of big data analysis, with clique detection being a key technology in social network mining. Also, detecting maximal balance clique in signed networks with positive and negative relationships is essential. In this paper, we present two algorithms. The first one is an algorithm, MCDA1, that detects the maximal balance clique using the improved three-way concept lattice algorithm and object-induced three-way concept lattice (OE-concept). The second one is an improved formal concept analysis algorithm, MCDA2, that improves the efficiency of memory. Additionally, we tested the execution time of our proposed method with four real-world datasets.


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Cite this article
[APA Style]
Yixuan Yang, Doo-Soon Park, Fei Hao, Sony Peng, Hyejung Lee, & and Min-Pyo Hong (2023). Detection of Maximal Balance Clique Using Three-way Concept Lattice. Journal of Information Processing Systems, 19(2), 189-202. DOI: 10.3745/JIPS.01.0094.

[IEEE Style]
Y. Yang, D. Park, F. Hao, S. Peng, H. Lee and a. M. Hong, "Detection of Maximal Balance Clique Using Three-way Concept Lattice," Journal of Information Processing Systems, vol. 19, no. 2, pp. 189-202, 2023. DOI: 10.3745/JIPS.01.0094.

[ACM Style]
Yixuan Yang, Doo-Soon Park, Fei Hao, Sony Peng, Hyejung Lee, and and Min-Pyo Hong. 2023. Detection of Maximal Balance Clique Using Three-way Concept Lattice. Journal of Information Processing Systems, 19, 2, (2023), 189-202. DOI: 10.3745/JIPS.01.0094.